Abstract
In this work we develop a general mathematical model and devise a practical identifiability approach for gastrointestinal stromal tumor (GIST) metastasis to the liver, with the aim of quantitatively describing therapy failure due to drug resistance. To this end, we have modeled metastatic growth and therapy failure produced by resistance to two standard treatments based on tyrosine kinase inhibitors (Imatinib and Sunitinib) that have been observed clinically in patients with GIST metastasis to the liver. The parameter identification problem is difficult to solve, since there are no general results on this issue for models based on ordinary differential equations (ODE) like the ones studied here. We propose a general modeling framework based on ODE for GIST metastatic growth and therapy failure due to drug resistance and analyzed five different model variants, using medical image observations (CT scans) from patients that exhibit drug resistance. The associated parameter estimation problem was solved using the Nelder-Mead simplex algorithm, by adding a regularization term to the objective function to address model instability, and assessing the agreement of either an absolute or proportional error in the objective function. We compared the goodness of fit to data for the proposed model variants, as well as evaluated both error forms in order to improve parameter estimation results. From the model variants analyzed, we identified the one that provides the best fit to all the available patient data sets, as well as the best assumption in computing the objective function (absolute or proportional error). This is the first work that reports mathematical models capable of capturing and quantitatively describing therapy failure due to drug resistance based on clinical images in a patient-specific manner.
Highlights
Gastrointestinal stromal tumors (GISTs) are the most common mesenchymal tumors of the gastrointestinal tract, with an incidence of 11-15 cases per million people per year
We describe GIST metastases to the liver, growth and therapy failure due to drug resistance, following a modeling strategy that considers three different cell populations, and the model was developed from mass balances for these cell populations, describing tumor growth, death and angiogenesis
In a first attempt to address the issue of describing such features in a quantitative manner, we developed a general mathematical model based on mass balances for tumor cells, studied five model variants given by specific parameters associated to cell populations and response to treatments, and applied a practical identifiability approach to these models using empirical data for two patients
Summary
Gastrointestinal stromal tumors (GISTs) are the most common mesenchymal tumors of the gastrointestinal tract, with an incidence of 11-15 cases per million people per year. It is estimated that 40-50% of GISTs are biologically malignant, and have spread to the liver or peritoneum at the time of diagnosis or primary surgery [1]. One of the molecular characteristics of these neoplasms is a gain of function mutation in the receptor tyrosine- kinase protein (KIT) (75-80% of cases) or the homologous receptor tyrosine kinase, platelet-derived growth factor receptor alpha (PDGFRA), accounting for 85-90% of gastrointestinal stromal tumors [2]. In addition to the primary mutation, secondary mutations have been identified in patients with advanced GIST pretreated with tyrosine kinase inhibitor. For most cases of resectable/non-metastatic GISTs cases treatment involves surgical resection, and tyrosine kinase inhibitor (TKI) therapy may be utilized to reduce tumor size before resection
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
